Discrete event network simulator (DES) is a fundamental service for the design, validation and optimization of various networked systems, including traditional computer networks and the recent LLM training/inference systems. Efficient DES is strongly demanded by the community yet not delivered. The reason is: while DES allows for node-level parallelism, its concurrency potential has been significantly under-utilized (usually <5%) due to the limited number of CPU cores. Meanwhile, GPUs, with thousands of cores, have long been overlooked in the design of DES systems due to the incompatibility between GPUs' SIMT architecture and DES's sequential model. In this paper, we aim to achieve ground-breaking performance improvement for DES by designing a novel GPU-driven simulation engine. By carefully manipulating the life cycles of network events and orchestrating them with long- and short-term alignments, we overcome the incompatibility barriers and established GeDES, a high-level parallel and cost-effective DES system. Extensive simulation experiments show that GeDES significantly boosts DES by 33-2400X speedups compared to the SOTA works Unison and DONS. The code is available at https://github.com/mobinets/GeDES.

Due to the maneuverability and cost-effectiveness, UAVs are visioned as a versatile assistant for networking in intelligent transportation systems. Per-packet path reconstruction that reveals the multi-hop forwarding path of each packet is a fundamental service for network management and optimization. Unfortunately, most existing reconstruction schemes are designed for static networks with stationary devices which cannot be applied to UAV networks with strong mobility and frequent topology changes. Besides, the limited energy and resources of UAVs make it even more challenging to reconstruct dynamic paths. To achieve accurate and low-cost reconstruction, we propose an incremental path reconstruction (InPrec) for dynamic UAV networks. With InPrec, each UAV only records the changes of its one-hop neighbors in a distributed and change-driven manner. The sink UAV then incrementally recovers the full path information by replaying the recorded changes to the historical paths. In this way, InPrec can adapt to the topology changes and avoid repetitive records of the common sub-paths in dynamic UAV networks. To explore the pros and cons of InPrec, we conduct measurement studies and theoretical analysis from perspective of information entropy. Simulations also demonstrate that InPrec can accurately reconstruct the paths with time-varying topology and reduces much redundancy (by 85% on average) of the state-of-the-art work. As far as we know, this is the first path reconstruction approach applicable to large-scale dynamic UAV networks.

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

Mobile CrowdSensing (MCS) is an emerging paradigm that employs massive mobile devices (MDs) to complete sensing tasks cooperatively. To provide ubiquitous MCS services, Unmanned Aerial Vehicle (UAV), featured by high agility and flexibility, becomes increasingly attractive as a powerful assistant for MCS to collect sensing data in hard-to-reach and infrastructure-restrained areas. Focusing on urban MCS scenarios where a tremendous amount of data needs to be uploaded by massive mobile devices, we propose a Three-Dimensional Multi-UAV assisted crowdsensing, termed 3DM, to collect sensing data efficiently in an infrastructure-free manner. Different from the existing methods, 3DM has two unique advantages: 1) removing the assumption of the ideal distributions of mobile devices and 2) fully exploiting the 3D flexibility to optimize the device matching and data transmission between UAVs and MDs. By employing a joint optimization metric that incorporates both energy efficiency and collection latency, 3DM dynamically maintains cost-effective UAV-MD links and 3D UAVs trajectories thus completes the collection tasks with less time and energy. Compared with the baseline algorithm and two state-of-the-art counterparts, extensive simulations demonstrate that 3DM saves at least 50% energy and 25% time of baseline while achieving 76% improvement of the sub-optimal competitor on overall utility.

Drones can be used for many assistance roles in complex communication scenarios and play as the aerial relays to support terrestrial communications. Although a great deal of emphasis has been placed on the drone-assisted networks, existing work focuses on routing protocols without fully exploiting the drones superiority and flexibility. To fill this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed regarding the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on the floating car data of Chengdu China, extensive experiments are conducted to examine the performance of the MNA-optimized drone-assisted VANET. The results demonstrate that our scheme outperforms its counterparts in terms of hop number, packet delivery ratio, and throughput.